Outlook for AI and ML in 2023

In recent years, India has made great advances in technology, notably in digital infrastructure. The broad use of the internet and cutting-edge technologies like artificial intelligence (AI), machine learning (ML), blockchain, robotics, 5G, etc has fueled India's Digital Revolution. As these technologies continue to evolve, they are expected to have a profound impact on the way businesses operate, making them more efficient and competitive.

The Crucial Role of Data Quality in Commercial AI Projects

Artificial intelligence is rapidly transforming the way organizations operate, and commercial AI projects are at the forefront of this change. One of the most crucial factors for the success of commercial AI and ML projects is data quality. Data quality is especially crucial in the age of generative AI and large language models. ChatGPT-like offerings require strong data quality in order to deliver precision results and avoid some of the negative outcomes that have been widely reported in the news. 

Poor quality data can lead to misleading conclusions, inaccurate predictions, and ultimately damage the efficacy of the project as a whole. Thus, high quality data is essential for training and fine-tuning AI models, as well as for generating accurate and reliable insights and predictions. Ensuring data quality involves several aspects, including data accuracy, consistency, and completeness. By prioritizing data quality, businesses can unlock the full potential of AI and ML technologies, leading to better decision-making and improved performance.

Data can be improved by following a few steps:

• Data cleansing for detecting and correcting inaccurate data

• Standardization for developing common templates for collecting data from multiple sources

• Automation should be used to discover irregularities in incoming data before they become problems

• Collaboration with various stakeholders from various departments would be essential to provide input on the type of information required as well as to verify its completeness and accuracy

Human Expertise is Central to the AI Equation

As technology advances, the role of humans with knowledge-based expertise in the development, implementation, and management of AI systems becomes increasingly crucial. From designing algorithms to training and fine-tuning models, human knowledge and input are essential to ensure that AI technologies are accurate, efficient, and effective. This is where "Human-in-the-Loop" aspect comes in, ensuring that humans collaborate with machine learning models and algorithms to achieve optimal results.

Human in the loop (HITL) refers to a process where humans are involved in decision-making, validation and supervision of machine learning models. In simpler terms, it means that there is always human oversight during the course of an automated task or AI-based project. While machines can process vast amounts of information quickly, they lack the context and intuition that humans bring to decision making. By incorporating human knowledge into AI systems, businesses can improve accuracy and achieve better outcomes. According to iMerit's State of MLOps survey, 95% of respondents agreed that human intelligence is the key to their efforts with 96% saying human labeling is critical for the success of their AI/ML data models.

Data annotation requirements are increasing in complexity, which increases the need for human expertise and intervention

As industries strive to automate their operations, the demand for dependable data annotation grows. Data annotation is the process of labeling or marking data so that machines can understand it easily. It may appear simple, but it involves a tremendous level of knowledge and attention to detail. Additionally, as per iMerit's report from State of ML Ops, 82% of data scientists believe data annotation requirements are becoming more complex than usual.

While developments in automated annotation technologies will likely contribute to the future of data annotation, human expertise will remain essential. This is not only because of the need to ensure quality assurance, handle uncertainty and complexity, and address ethical concerns but also because of the unique domain specific knowledge that only humans can provide.

Commercial AI become a key to solving edge cases with human expertise

Commercial AI is constantly evolving, and its applications are spreading across multiple industries. The goal is to leverage AI technologies to streamline processes, uncover insights from data, improve decision-making, enhance customer experiences, and drive innovation and growth in businesses and industries.

While AI technology has advanced significantly and is capable of performing a wide range of tasks autonomously, it often benefits from human guidance and expertise to achieve optimal results. Below are a few examples of how human skills are required in Commercial AI across various industries to address edge cases and enhance business performance.

• Healthcare: There are times where AI applications scanning MRI images may fail to identify signs of serious illness such as tumors. Despite the fact that the model may say everything is ok, sometimes a human expert is needed to find issues, such as extremely small sized tumors.

• Finance: AI is used to identify unusual transactions and detect potential fraud, ensuring the security and integrity of financial systems. However, While machines can detect patterns in data, they may not be able to identify suspicious behavior without expert input. By leveraging the knowledge of experienced fraud investigators alongside advanced machine learning algorithms, banks can better protect themselves from fraudulent activities.

• Manufacturing: AI-powered systems can identify anomalies in production processes or equipment performance, enabling businesses to optimize efficiency and reduce downtime. While AI can detect defects in products based on predetermined criteria, a human expert can provide additional insight by identifying flaws that may not have been considered or anticipated by the algorithm.

• Retail: AI that analyzes customer behavior and preferences needed to quickly adapt during COVID, as the volume and types of online purchases radically changed as the pandemic forced buying habits to change. Recommendation systems were taxed to change quickly as well, which was only possible due to human intervention.

These technologies are revolutionizing the way businesses operate and impacting our lives in ways we never thought possible. From healthcare to finance, AI and ML have already made a significant impact on various industries, paving the way for more efficient processes, better decision-making capabilities, reduced costs, and improved stakeholder experiences. As we have seen, AI and ML have great promise and have the capacity to alter industries in various ways. However, as promising as these technologies are, they are not yet perfect.

While AI and ML may appear as a universal solution for businesses seeking process optimization and competitive advantage, it is crucial to recognize that human expertise remains irreplaceable. By combining the strengths of both humans and machines, businesses can leverage the true potential of these technologies while ensuring they remain aligned with their goals.

Author:

Sudeep George, Vice President – Engineering, iMerit